Electronic component classification

ABSTRACT

A system and method of electronic component authentication or component classification can reduce the vulnerability of systems (e.g., satellites, weapons, critical infrastructure, aerospace, automotive, medical systems) to counterfeits. Intrinsic deterministically random property data can be obtained from a set of authentic electronic components, processed, and clustered to create a classifier that can distinguish whether an unknown electronic component is authentic or counterfeit.

BACKGROUND OF THE INVENTION

The present invention relates to classification or authentication ofelectronic components.

Electronic components are widely counterfeited. It is estimated thatbetween 5% and 25% of all electronic components include at least onecounterfeit component. Some have estimated that counterfeit electroniccomponents cost the industry as much as one-hundred billion dollarsevery year.

A counterfeit electrical component generally refers to a component thatimitates a genuine electrical component. Counterfeit components caninclude components created from substandard or cheaper components. Forexample, counterfeit components may substitute an integrated circuitharvested from an electronic trash dump or utilize a die from asubstandard new part in an authentic looking package. Counterfeitcomponents can also include older model components or used componentsthat are packaged to imitate a newer model or new component.

Some different categories of counterfeits are described in Table 1.

TABLE 1 Counterfeit Electronic Component Categories Type of CounterfeitElectronic Component Description Trivial These are empty plasticpackages with topmarks Packaging appearing to be authentic, or remarkedparts that share Mimicry only external visual traits with the authenticpart and are trivial to detect. One problem is when a small number ofcounterfeits are present in each reel or tube so that testing a samplingmakes it unlikely the counterfeits are detected. Salvaged and These areauthentic parts that may even have the Refurbished original markings buthave been recovered from electronic waste dumps where they may have beensubjected to excessive heat or exposed to harsh chemicals. Thesecomponents are de-soldered from discarded PC boards and reprocessed toappear new. The chip may in fact authentic, but have degradedperformance due to environmental exposure and having been used well intoor past their service life. Non-functional parts may also be recoveredfrom salvaged electronics waste. Re-binned and This type of counterfeitdevice may contain the wrong Remarked die internally and a remarked ornewly manufactured package. In other cases these are parts that wereauthentic, and perhaps have never been used (so can be classified as“new”), but have their markings changed to reflect a higherspecification of an identical function. Factory Rejects Factory rejectsand pilot runs can be recovered from and Scrap the scrap heap for asmall bribe, and given authentic markings and resold as new. In order toavoid detection, workers often replace the salvaged scrap withphysically identical surrogate packages, thus foiling attempts to auditthe scrap trail. Manufacturing rejects are often nearly functional, andwith the true manufacturer marking they have the appearance of authenticcomponents. Illegitimate Second-sourcing is a standard industry practiceto Second-Sourcing create pin-compatible replacements for popularproducts in order to encourage price competition and increase sourcingoptions. The practice become illegitimate when inferior parts areremarked with the logos of premium brands. Ghost-Shift These parts aremanufactured from new but illegally Parts acquired die and may come fromthe legitimate manufacturers rejects. The parts are created on the exactsame fabrication facility as authentic parts, but run by employeeswithout authorization of the manufacturer and never logged on the books.These parts may be assigned a lot code identical to a legitimatemanufacturing run, but will not have undergone the entire testingprocess.

Conventional electronic component authentication and authenticationmethods are either ineffective or impractical because of their time tocomplete or cost to execute. Among the emerging electronic componentauthentication and authentication technologies there are essentiallythree broad categories of electronic component authenticationmethods: 1) authentication based on difficult-to-reproduce physicalfeatures, 2) verification of products with unique identifiers, and 3)direct authentication.

Modifying a component to include a difficult-to-reproduce physicalfeature (e.g., holograms on credit cards) can impede counterfeitcomponent construction. However, difficult-to-reproduce physicalfeatures historically have eventually become “easy-to-reproduce” whenthe financial incentives have justified the effort.

Unique identifiers can be used to serialize and track electroniccomponents using a database for verification. However, mimicking aunique identifier of a known genuine component is not a significantimpediment to counterfeit construction. Further, serializing andtracking unique identifiers can become ineffective if the supply chainis flooded with multiple counterfeits that carry the so-called uniqueidentifiers.

Direct authentication relies on intrinsic deterministically randomproperties of the component, instead of an artificial feature added tothe product. Mimicking an intrinsic deterministically random but randomproperty of a component is much more difficult than mimicking anartificial feature that is added to the product for authentication.Although direct authentication can be more difficult to circumvent forthis reason, it has its own set of issues (for example, if the intrinsicproperty is not truly random and can be cloned then it will providelittle protection from counterfeiters).

Some direct authentication techniques are destructive. That is, the actof determining the intrinsic deterministically random properties of thecomponent, in some cases, destroys the component. This can still beuseful in some situations where it may be possible to destructivelyauthenticate a subset of components from a group in order toauthenticate the group as a whole. However, depending on the context,authenticating a statistically appropriate sample size may not bepossible or may be cost prohibitive. For example, it may be impracticalto destructively authenticate a statistically appropriate sample size ofa group of components for use in a national security or public safetycontext.

Some emerging direct authentication methods of electronic componentauthentication are not destructive, but instead use intrinsicdeterministically random or physically unclonable characteristics tocreate a “fingerprint” or tag for each component that is recorded,serialized, and tracked through a database. This method can be effectiveprovided that sources and supply chain logistics for the components aretrusted and the database is immune from attacks and corruption.Depending on the specific “fingerprint” employed (e.g., packageproperties) this type of direct authentication may not detect componentsthat contain hardware Trojans or have been weaponized in some otherfashion.

SUMMARY OF THE INVENTION

The present invention is directed to classification of electroniccomponents from an untrusted source using direct authentication.Classification may include classifying a component as authentic orcounterfeit. Classification may also include classifying a component asa specific component class or an unknown component class. An electroniccomponent from an untrusted source can be classified based on thesimilarity or differences of one or more of its intrinsicdeterministically random properties relative to one or more intrinsicrandom properties of one or more authentic electronic components. Thisclassification can be done non-destructively, quickly, and at a lowcost.

In one embodiment, a classifier can be created to classify an electroniccomponent from an untrusted source. The classifier can be created byobtaining measurements from a number of trusted components, where themeasurements include signals indicative of an intrinsicdeterministically random property of each component, i.e., a time domainpower consumption signal, a time domain electric field signal, a timedomain magnetic field signal, a spatial domain electric field signal, ora spatial domain magnetic field signal. In one embodiment, the signalsmeasured are indicative of the unique electrical noise signature of eachcomponent.

By building the classifier based on an intrinsic deterministicallyrandom property of a training set of trusted components the classifiercan identify whether an intrinsic deterministically random property ofan untrusted component is similar to the intrinsic deterministicallyrandom property of one (or a group) of the trusted components from thetraining set, and if so, which one(s). For example, the classifier candetermine whether the intrinsic deterministically random noise signatureof an unknown component is similar to the intrinsic deterministicallyrandom noise signature of one of the trusted components in the trainingset used to create the classifier.

Measurements used to build a classifier may be obtained from a trainingset of trusted components and the classifier may be built to classifyunknown components according to a variety of criteria. Multiplemeasurements may be obtained from a trusted component in the trainingset. The training set of trusted components may include components frommultiple component classes and may include multiple components from eachcomponent class. A component class may differentiate between componentsmanufactured at a particular point in time, with a specific set ofmanufacturing equipment, or otherwise manufactured under a specific setof conditions. That is, in some embodiments, components that sharefunctionality may not necessary share the same component class. Acomponent class may differentiate between different generations ofcomponents, wherein some or all of the components of a component havebeen changed, but the same or similar functionality is provided. Acomponent class may differentiate between one, or a group of trustedcomponents in the training set that performs a substantially differentfunction from a different one or different group of trusted componentsin the training set.

One embodiment of a method of component classification includesmeasuring or sensing signals indicative of deterministic noisesignatures that are intrinsically unique to each member of a set oftraining components, segmenting those signals to create vectorrepresentations of the noise signatures, processing the vectors (i.e.,by mathematical transformations to specific feature domains (e.g., FFT)and dimension reduction), and clustering the processed vectors todevelop a vector set that represents similar attributes within aspecified set representing a class of electronic components and yetunique relative to other sets that are members of different classes ofelectronic components. The uniqueness of the attributes for each classof components can be used in discriminating counterfeit and authenticparts and can be used to classify or authenticate unknown componentsfrom untrusted sources.

The signals of the trusted components can be processed and clustered tocreate the classifier. A similarly processed signal from an unknownelectronic component can be input in the classifier and the classifiercan output a classification of the unknown electronic component.Depending on how the classifier is created, the classifier may classifythe unknown component as authentic, a specific class, counterfeit, or anunrecognized class. The classifier may provide a confidence valueassociated with its classification.

The trusted components for creating the classifier can be obtained froma trusted source or, if obtained from an untrusted source, theirtrustworthiness can be established utilizing known electronic componentauthentication electronic component authentication techniques. Forexample, after measuring a signal indicative of an intrinsicdeterministically random property, the component may undergo destructivedirect authentication to establish the authenticity of the component.Through use of intrinsic deterministically random noise signature andsignal processing, destructive authentication of one or a few untrustedcomponents can be used to create a classifier that classifies withsignificant confidence for untrusted components of that class.

A signal indicative of an intrinsic deterministically random propertycan be processed in a variety of different ways. In one embodiment, thesignal is segmented into a vector and transformed into a feature vectorin the frequency or a different time-independent domain. For example,the transformation can be a discrete Fourier transform, discrete cosinetransform, Hilbert transform, real cepstrum, or a hybrid of severaldifferent transforms. The specific transformations can be selected basedon achieving the desired clustering behavior.

The feature vector may be reduced in dimension by principal componentanalysis or some other dimension reduction technique. Reduction ofdimensionality can include ordering the feature vectors from greatestvariance between feature vectors to lowest variance between vectors. Lowvariance vectors below a threshold may be discarded to reducedimensional space.

A clustering algorithm can be applied on a set of training data, i.e., aset of feature vectors, to produce a classifier. A transform matrix mapsa sample from an unknown component, i.e., a feature vector of an unknowncomponent, into the same space as the training data and the classifiercan determine the most likely class of the unknown sample.

Processed signals of the trusted components can be clustered in avariety of different ways to create a viable classifier. For example,the clustering analysis can be a shared nearest neighbor analysis,k-means analysis, modified shared nearest neighbor analysis, or anothertype of clustering analysis. The clustering analysis may includedefining a set of points with tolerances around that point in order todefine specific classification areas. In one embodiment, processedsignals may be sub-clustered before principal component analysis so thatthe principal component analysis focuses on the variation betweenclasses, instead of the variation inside of each class.

One embodiment of an electronic component authentication system includesa control system, a component interface, storage, and a sensor. Thecomponent interface can include a socket for interfacing with theelectronic component. The interface may include providing power andother signals to the electronic component. In one embodiment, thecomponent interface includes connections for rail voltage, ground, andan external clock. The sensor may be a power consumption sensor,electric field, or magnetic sensor. The sensor may be integrated withthe control system or circuit interface, or a separate component thatcommunicates with the circuit interface or control system. The storagecan store a codebook or classifier for use in classifying an unknownelectronic component. The control system may be configured to build aclassifier or codebook and to use the classifier or codebook. Buildingthe classifier may include programming the control system to receivemeasurements indicative of an intrinsic deterministically randomproperty from trusted components, process those measurements, and builda classifier or codebook based on those measurements. Using theclassifier may include programming the control system to receivemeasurements indicative of an intrinsic deterministically randomproperty from an unknown component from an untrusted source, processthose measurements, and classify the component using the classifier orcodebook stored in memory.

One embodiment of an electronic component authentication processincludes receiving a plurality of components that each purports to be acomponent class, and determining whether each component class has beenenrolled in an electronic catalog. For each unenrolled component class,conducting a pre-enrollment process to capture intrinsicdeterministically random property data about one or more of thecomponents in that component class and determining whether thecomponents are authentic using destructive authentication. For authenticcomponents, conducting enrollment process whereby the intrinsicdeterministically random property data is stored in the electroniccatalog database so that future components in that component class canbe authenticated using the electronic catalog. For counterfeitcomponents, documenting and quarantining the components and selectingone or more additional components from that component class to undergocapture of intrinsic deterministically random property data andauthentication. For each component in an enrolled component class,determining whether the component is authentic by measuring a signalindicative of an inherent property of the component, such as anintrinsic deterministically random noise signal, and determining whetherthe component is authentic by comparison to the inherent property datain the electronic catalog for that component class. For authenticcomponents, identifying the component as authenticated inventory orplacing the component in authenticated inventory. For counterfeitcomponents, documenting and quarantining the component.

The electronic component authentication method and system can enable100% examination of electronic components without modifying thepackaging (e.g., inserting or attaching tags or taggants), altering thedesign (e.g., die level edits or functionalizations, such as usingphysically unclonable functions to generate identifying signatures), orinserting new technology into the manufacturing process. The electroniccomponent authentication method and system can address electronicsreliability where there are no trusted foundries to produce parts, orparts are acquired from international chip brokers and secondarymarkets.

These and other objects, advantages, and features of the invention willbe more fully understood and appreciated by reference to the descriptionof the current embodiments and the drawings.

Before the embodiments of the invention are explained in detail, it isto be understood that the invention is not limited to the details ofoperation or to the details of construction and the arrangement of thecomponents set forth in the following description or illustrated in thedrawings. The invention may be implemented in various other embodimentsand of being practiced or being carried out in alternative ways notexpressly disclosed herein. Also, it is to be understood that thephraseology and terminology used herein are for the purpose ofdescription and should not be regarded as limiting. The use of“including” and “comprising” and variations thereof is meant toencompass the items listed thereafter and equivalents thereof as well asadditional items and equivalents thereof. Further, enumeration may beused in the description of various embodiments. Unless otherwiseexpressly stated, the use of enumeration should not be construed aslimiting the invention to any specific order or number of components.Nor should the use of enumeration be construed as excluding from thescope of the invention any additional steps or components that might becombined with or into the enumerated steps or components. Any referenceto claim elements as “at least one of X, Y and Z” is meant to includeany one of X, Y or Z individually, and any combination of X, Y and Z,for example, X, Y, Z; X, Y; X, Z; and Y, Z.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of one embodiment of a method ofelectronic component classification.

FIG. 2 illustrates one embodiment of a method of counterfeit mitigation.

FIG. 3 illustrates a representative diagram of an enrollment process foran electronic component classification system.

FIG. 4 illustrates a flowchart for enrollment for an electroniccomponent classification system.

FIG. 5 illustrates a flow diagram of featurization of an intrinsicdeterministically random noise signal including segmentation,transformation, and dimension reduction.

FIG. 6 illustrates a flow diagram of featurization of an intrinsicdeterministically random noise signal including segmentation, hybridtransformation, and dimension reduction.

FIG. 7 illustrates a visualization of an exemplary classifier.

FIG. 8 illustrates a two-dimensional visualization of an exemplaryclassification of an unknown component.

FIG. 9 illustrates another two-dimensional visualization of an exemplaryclassification of an unknown component.

FIG. 10 illustrates another two-dimensional visualization of anexemplary classification of an unknown component.

FIG. 11 illustrates a two-dimensional visualization of an exemplaryclassification of five unknown components.

FIGS. 12A-D illustrates four exemplary spy graphs.

FIG. 13 illustrates an exemplary spy graph of five components.

FIG. 14 illustrates an example of sub-clustering.

FIG. 15 illustrates an exemplary circuit diagram of a personality board.

FIG. 16A-H illustrates an exemplary circuit diagram of an interfaceboard.

FIG. 17 illustrates an exemplary diagram of a magnetic field sensor.

FIG. 18 illustrates a photograph of an exemplary magnetic field sensor.

FIG. 19 illustrates an exemplary circuit diagram of an electric fieldsensor.

FIG. 20 illustrates a photograph of an exemplary electric field sensor.

FIG. 21 illustrates a photograph of an exemplary stationary stand for asensor.

FIG. 22 illustrates a photograph of an exemplary x-y table for a sensor.

FIG. 23 illustrates a block diagram for one embodiment of an electroniccomponent authentication system.

FIG. 24 illustrates one embodiment of an electronic componentauthentication system.

FIG. 25 illustrates multiple personality boards.

FIG. 26 illustrates a diagram of a representative integrated circuit.

FIG. 27 illustrates a block diagram of one embodiment of aclassification system

FIG. 28 illustrates a block diagram of another embodiment of aclassification system.

DESCRIPTION OF THE CURRENT EMBODIMENT

The present invention is directed to classification of electroniccomponents from an untrusted source. Classification may includeclassifying a component as authentic or counterfeit. Classification mayalso include classifying a component as a specific component class or anunknown class. An electronic component from an untrusted source can beclassified based on the similarity or differences of its intrinsicdeterministically random properties relative to the intrinsicdeterministically random properties of authentic electronic components.This classification can be done non-destructively, quickly, and at a lowcost.

Intrinsically random properties of components exist that are unique toeach individual component. Anti-counterfeiting methods based on usingthese properties to tag, serialize, and track components are emergingfor assuring the integrity of the supply chain from trusted sources.However, these methods do not account for components acquired fromuntrusted sources. There is a prevalence of counterfeit componentssubstituted for authentic components from an untrusted foundry or notdirectly acquired from a trusted foundry.

An intrinsic deterministically random noise signal exists in alldigital, analog, and passive components. These signals can beparameterized to form identity vectors for designated classes ofelectronic components due to signal similarities within a given class.Parameterization of signal feature sets can be optimized to createmulti-dimensional identity vectors that cluster in a way tostatistically minimize false positive and false negative rates foridentification of authentic and unaltered analog. Different clusteringalgorithms can be selected based on properties of said unknownelectronic components. For example, certain clustering algorithmsprovide better clustering behavior for a given set of features based onproperties of the unknown electronic component vectors category. Thatis, different clustering algorithms may be appropriate for passive,active, digital, analog, resistor, capacitor, etc. components. Theclustering algorithm can be chosen based on the dynamic time domaincharacteristics of the acquired signal such as: modulation, impulsebehavior, and types of unwanted noise.

The electronic component authentication method can include determiningn-dimensional identity vectors that share sufficient similarities forall chips or components within each class of authentic components tocluster with low Type I and Type II errors. This allows a low cost,rapid, nondestructive authentication and validation of chips fromnon-trusted sources. It also can enable 100% examination of chipswithout modifying the chip packaging (e.g., inserting or attaching tagsor taggants), altering the chip design (e.g., die level edits orfunctionalizations such as using physically unclonable functions togenerate identifying signatures), or inserting new technology into themanufacturing process. The electronic component authentication methodcan address the issue of electronics reliability where there are notrusted foundries to produce the parts and instead parts must beacquired from international chip brokers and secondary markets wherethere may be vulnerabilities in the supply chain.

One advantage of some embodiments of the electronic componentauthentication method is that chips from trusted sources are notrequired. Only a very small number of components (e.g., as small as asingle component that is initially trusted) can be used to create aclassifier that can authenticate components already in the supply chainthat have not been tagged and are not necessarily from trusted sources.

Electronic components have intrinsic data properties, such as uniqueelectrical noise signatures that provide a fingerprint of the specificcomponent. This is because the noise is the result of fabricationprocess differences between the same components in the same componentlot. By measuring deterministic noise signatures that are intrinsicallyunique to each integrated circuit, creating an n-dimensional vectorrepresentation of the noise signatures; and applying a clusteringalgorithm to construct the vector set that represents the uniqueattributes counterfeit and authentic parts can be discriminated. Theprocessed vectors can be used to create a codebook from which authenticcomponents are recognized from a quick and simple noise measurement.

The die in integrated circuits have unique noise signatures which can beused to discriminate between authentic and counterfeit parts based onstatistical clustering of n-dimensional feature vectors associated witheach IC part number, manufacturing lot, and die layout. The noisemeasurement component can isolate the IC under test from extrinsic noisesources and measure the IC noise signature. A training set is composedof a number of trusted integrated circuits of the same part number. Atraining set is used to collect intrinsic waveforms that are segmentedand parameterized to create an n-dimensional vector representation ofthe authentic integrated circuits for a given part number. A codebookcan be created from the n-dimensional vectors representations of eachintegrated circuit for all the part numbers to be tested to determineauthenticity. Chips can degrade over time and change their noisecharacteristics, so it may be possible to identify counterfeit chipsthat are manufactured with the same material, but at different points intime.

Referring to FIG. 1, one embodiment of an electronic componentauthentication method is illustrated. The method generally includesbuilding a classifier 102 using authentic electronic components 106, andclassifying 104 unknown electronic components 106. The authenticelectronic components can originate from a trusted source, in which casethe components may be assumed authenticated. Further, electroniccomponents from an untrusted source may be authenticated and used tobuild the classifier. For example, a subset of components from a largeshipment of components from an untrusted source may be authenticated andused as the basis for a classifier. For example, intrinsic noise signalsmay be measured and known destructive electronic componentauthentication analysis may be performed to verify the authenticity ofthe subset of components. The measurements from any counterfeitcomponents are not used to build the classifier.

One embodiment of building a classifier generally includes sensing 110,segmenting 112, transforming 114, reducing dimension 116, and clustering118. In general, the system senses a signal such as a power, current, orEM field for a plurality of components. In some embodiments, multiplesignals for each component are sensed. Each of the signals is segmentedto create a feature vector. These feature vectors can be transformed andreduced in dimension. The feature vectors can be clustered to create aclassifier.

The sensing step 110 can include measuring any signal that is indicativeof the intrinsically random properties of a component. For example, thefollowing can be indicative of intrinsically random properties of acomponent: power draw over time, current draw over time, electric fieldover time and/or space, or magnetic field over time and/or space. Theseexample signals may be indicative of the inherent noise signature of thecomponent when measured during an idle state.

The sensing can be conducted while the component runs in a defaultbackground mode or an idle state. In one embodiment, the component isconnected to a constant voltage source, ground, and an external clocksignal. As the external clock signal changes, the intrinsic randomproperties of the component affect the signals being sensed. Forexample, the amount of current draw on Vcc changes when the externalclock signal transitions. Further, the electric field and magnetic fieldmay be affected by external clock signal transitions. The sensing may beconducted after reaching steady state.

Segmenting each signal may include converting an analog signal into aset of discrete values that represent the signal. The segmented signalmay be referred to as a feature vector. In some embodiments, sensing andsegmenting may be conducted as a single step where measurements of asignal are obtained and stored in a feature vector.

The feature vector can be transformed 114 into the frequency or adifferent time independent domain. In one embodiment, each featurevector is transformed with a discrete fourier transform (DFT) or fastfourier transform (FFT). FIG. 5 illustrates a fast fouriertransformation of a captured waveform 502 to a feature vector 504 andapplication of principal component analysis to reduce the dimensionalityof that vector 506.

In alternative embodiments, each feature vector may be transformed witha discrete cosine transform, Hilbert transform, real cepstrum, waveletcoefficients, or a hybrid of several different transforms. FIG. 6illustrates one embodiment of a hybrid transformation of a capturedwaveform 602 to a feature vector 604 and application of principalcomponent analysis to reduce the dimensionality of that vector 606. Thehybrid transformation may include full transformations of the featurevector using different transformation methods, where the results of eachtransformation are concatenated together. By conducting the hybridtransformation in the same manner for each component signal, additionaldata that can be helpful in distinguishing between component classes maybe included. For example, by using a hybrid transformation it may bepossible to increase component classification confidence because certaintransformations may provide more or less differentiation betweendifferent classes. For example, it may be easier to distinguish betweentwo component classes using a Hilbert transform, but easier todistinguish two other component classes using a fast Fouriertransformation. Use of the hybrid transformation increases the amount ofdifferentiation between feature vectors, which can increases confidencein classification.

The dimension of the feature vector can be reduced using essentially anyknown dimension reduction technique 116. For example, principalcomponent analysis (PCA) can be conducted to reduce dimensionality. PCAtransforms the feature vectors into a space where the greatest variancebetween samples is in the first dimension, the next greatest variance inthe next dimension and so on. By organizing the feature vectors bygreatest variance, dimensions where the least variance between samplesoccurs can be discarded in order to enable comparisons in a lowerdimensional space with conventional distance metrics. Although thecurrent embodiment implements PCA, other non-linear analysis techniquesmay be employed instead such as self organizing maps or other manifoldbased learning algorithms. In one embodiment, principal componentanalysis on the feature vector to reduce dimensionality of the featurevector includes organizing the feature vector by variance and discardingdimensions where the variance is below a threshold. In anotherembodiment, principal component analysis on the feature vector to reducedimensionality of the feature vector includes organizing the featurevector by variance and discarding all but a predefined number ofdimensions that have the highest variance.

In one embodiment, processed signals may be sub-clustered beforeprincipal component analysis and clustering analysis. In general,principal component analysis finds the greatest variation among a set ofvectors, where each vector represents a unique class. In someembodiments, multiple measurements are obtained for components withineach class so the data may include multiple vectors per unique class(i.e., 100 vectors per class). In this way, each unique class can bemade up of some smaller number of sub-classes (i.e., about 5 sub-classesper class). By conducting principal component analysis on all of thevectors, some of the variation is intra-class/sub-class, meaning thatthe principal component analysis is optimizing partially for thedifferences in measurements of the same class. For example, a moredesirable result may be obtained for a given class by feeding one vectorfor each sub-class, instead of many vectors for each sub-class. Forexample, if there are 5 sub-classes, by inputting only 5 vectors forthat chip class into the PCA algorithm (one for each sub-class), a moreaccurate result may be achieved by feeding in many vector measurementsfor each sub-class. By pre-clustering the data into sub-classes, andoutputting a set of representative vectors for those sub-classes foreach class via hierarchical clustering, we can avoid or reduce focus inthe principal component analysis on intra-class variations. One benefitof this approach is that by inputting fewer sub-class vectors into thePCA algorithm, the algorithm can be run quicker and can focus onoptimization for the differences in classes and sub-classes instead ofthe variability in a large number of sample measurements.

Put another way, in one embodiment before conducting a principalcomponent analysis on all vectors, a sub-clustering analysis can beperformed in order to find representative samples for each class so thatwhen PCA is conducted, the focus can be on the variation betweenclasses, instead of the variation inside of each class. FIG. 14 shows arepresentative visualization to illustrate this concept. In the depictedembodiment, instead of conducting PCA on all 12 vectors 1402, threerepresentative vectors 1404 are identified—one for each class and PCA isapplied to only those three vectors. In alternative sub-clusteringembodiments, multiple vectors may be identified for each class insteadof a single vector. Further, in the depicted embodiment, the identifiedvector represents an average of the class vectors. In alternativeembodiments, averages between nearby vectors within a class may beidentified or a single vector may be selected as a representative vectorinstead of creating an artificial compromise vector.

One embodiment of using the classifier on an unknown component or samplegenerally includes the following steps: sensing 120, segmenting 122,transforming 124, applying dimension reduction 126, and classifying 128.In general, the processing of the unknown component proceeds similarlyto the processing of the authentic electronic components duringclassification. Where principal component analysis is utilized to buildthe classifier and a transform matrix is created, that same transformmatrix may be used to order and reduce the dimension of the unknownsample, as opposed to running an entire PCA analysis. The classify stepgenerally includes comparison of the processed signal of the unknowncomponent in the feature space to the processed signals of the authenticcomponents in the feature space. Some simplified examples of thiscomparison are illustrated in FIGS. 7-10, which will be discussed inmore detail below.

FIG. 7 illustrates a plot of the first two dimensions of an exemplaryset of processed vectors of authenticated electronic components forenrollment. In the illustrated example, there are twelve signals plottedfrom three component classes. Each circle, triangle, or star representsone signal, or processed vector. Although FIG. 7 illustrates the firsttwo dimensions of these vectors, it is possible that the vectors mayinclude many additional dimensions. For example, in one embodiment,after dimension reduction, the vectors include 20 dimensions. The threedifferent areas 702, 704, 706 each encompassing four vectors, illustratea representational boundary for the three component classes. In theillustrated embodiment, a modified shared nearest neighbor clusteringanalysis generates the thresholds outlined by areas 702, 704, 706. FIG.8 illustrates how an unknown component 802 can be mapped into thisfeature space in order to be classified. In FIG. 8, the unknowncomponent 802 falls within the boundaries of the class 1 area 702 andtherefore can be classified as a class 1 component. In FIG. 9, adifferent unknown component 902 falls within the boundaries of the class3 area 706 and therefore can be classified as a class 3 component. InFIG. 10, a different unknown component 1002 falls outside the boundariesof all of the known classes 702, 704, 706 and therefore can beidentified as counterfeit or as unknown component class.

The modified shared nearest neighboring clustering algorithm starts withstandard k nearest neighbor, where we create a graph based on thedistance between all pairs of points in the data set and a link iscreated for the k closest point for every point. We then remove allnon-symmetric links and weight the graph, which yields a standard sharednearest neighbor clustering graph. The enhanced version then alsoremoves points it believes to be noise, that is links and points withlow connectivity. We then select the highest connectivity points andform our clustering graph with them and all points directly connected tothem.

Although the current embodiment is described in connection with a sharednearest neighbor algorithm, in alternative embodiments a differentclustering analysis may be conducted. For example, unmodified sharednearest neighbor or a k-means clustering analysis may be implemented.

By clustering multiple classes, the electronic component authenticationsystem can simultaneously detect whether a component is a number ofdifferent classes simultaneously. Without clustering, it may only bepossible to determine whether an electronic component is a single classat a time, which can make electronic component authentication timeconsuming.

The boundaries of the component class can be set by the clusteringanalysis 118. Although FIGS. 7-10 illustrate two dimensional plots, itshould be understood that the boundaries of each component class can bedefined in feature space commensurate with the number of dimensions ofthe feature vectors. For example, if the feature vectors are threedimensions, then the boundaries for each component class may berepresented in three dimensions. If the feature vectors are twentydimensions, then the boundaries for each component class may berepresented in twenty dimensions. Further, it should be understood thatFIGS. 7-10 are representative diagrams designed to allow visualizationof classification—the classifier or electronic component authenticationsystem need not provide or generate such visualization. The distancebetween an unknown component and the authenticated training data can becalculated without visualization by a controller and the classificationcan be determined mathematically based on the distance. For FIGS. 7-10where only two dimensions are in play, Euclidian distances are helpfuland accurate. In alternative embodiments with a larger number ofdimensions, distance calculations between vectors may be conducted usingcosine distance calculations, or other non-Euclidean distancecalculation techniques.

FIG. 11 illustrates the results of another exemplary shared nearestneighbor cluster analysis with four samples of five different componentclasses ey-am, ey-ac, gr, jb, and jt. In addition, five unknown samplesthat each purport to be one of the five component classes are mappedinto the feature space. Again, the illustrated visualization displaystwo dimensions of the reduced dimension vector. In the illustratedembodiment, two chips or components that differ only by their thermalperformance specification, ey-ac and ey-am are mapped to the featurespace. As shown, using only two dimensions, it may be difficult todifferentiate between ey-ac and ey-am classes. However, the ability andconfidence in differentiation between ey-ac and ey-am chips can beincreased by factoring in additional dimensions of the feature vector.For example, considering twenty different dimensions may enable aclassification system to differentiate between not only differentclasses that have different functionality, but also different chipclasses that share identical functionality but have differentperformance characteristics, such as thermal performance. FIG. 13 showsa spy graph visual representation of a classifier for the same fivecomponent classes.

In the current embodiment, the clustering and classifier results of FIG.11 are for four specimens from each of five slightly different chipclasses. In alternative embodiments, additional or fewer specimens foradditional or fewer chip classes may be obtained. In the currentembodiment, all of the chips were based on the same central processingunit with different modules, and two of the chips were identical withthe exception that one has a preferred thermal performance (and thus isa more expensive part but manufactured on the same line). The classifiertrained on one of the specimens of each chip class and then processedthe power signals from the other 3 specimens in each class as anunknown.

FIGS. 12A-12D illustrates several spy graph visualizations ofrepresentative classification results for three components, two of whichare related RFa, RFb and one of which is unrelated GZ60. The spy graphsshow the range of possible results depending on how the parameterizationis applied to create identity vectors. FIG. 12A illustratesnon-distinguishing parameterizations, whereby the intrinsic propertydata cannot be used to meaningfully distinguish between the threecomponents. FIG. 12B illustrates unique individual parameterizationwhereby each and every part from a trusted source has unique physicallyunclonable features that can be measured and recorded. In this scenario,the unique physically unclonable features for every component allowtagging, serialization and tracking for every component—even relatedcomponents can be easily distinguished. FIG. 12C illustrates poor classparameterization where differentiating between related components can beaccomplished confidently, but it may be challenging to differentiatebetween unrelated components. FIG. 12D illustrates unique classparameterization where differentiating between unrelated components canbe accomplished confidently, but it may be challenging to differentiatebetween related components. In this configuration, by measuringphysically unclonable feature fingerprints of a few authentic parts fromeach class and enrolling those classes in the classifier it is possibleto identify the class of component.

FIG. 23 illustrates one embodiment of a electronic componentauthentication system or component classification system 2300. Thesystem generally includes a control unit 2302, a component interface2304, a sensor 2306, and storage 2308. The component interface 2304enables a component to be connected to the electronic componentauthentication system for examination. The sensor can measure anintrinsic noise waveform of the component and communicate to the controlsystem. The control system can process the intrinsic noise waveform. Thecontrol system can build a classifier by processing and clusteringmultiple intrinsic noise waveforms of authentic components. The controlsystem can classify an unknown component by processing its intrinsicnoise waveform or other intrinsic property data and inputting it to theclassifier. In one embodiment, the control unit can provide aclassification of an unknown component and a confidence rating.

FIG. 24 illustrates another embodiment of an electronic componentauthentication system or component classification system 2400. Theelectronic component authentication system may include a developmentboard 2402, an interface board 2404, one or more personality boards2406, and a host computer 2408. The development board 2402 and hostcomputer 2408 can act in concert as a control unit and storage. Thepersonality board can provide an interface for an electronic componentand the interface board may include a sensor for collecting intrinsicproperty data about the electronic component. In concert, thepersonality board, interface board, development board, and host computerconstitute hardware that can be used to acquire intrinsic property data,such as the power consumption signature of an electronic component.

FIG. 27 illustrates a block diagram of another embodiment of anelectronic component authentication system or component classificationsystem 2700. The system includes a host computer 2702, a dataacquisition system 2704, and a device under test 2706. The host computercan be connected to the data acquisition system by a USB bus. Power andcontrol signals can be transferred between the data acquisition systemand the device under test.

A block diagram of one embodiment of the electronic componentauthentication system or component classification system 2400 isillustrated in FIG. 28. The electronic component authentication systemmay include an FPGA development board 2402 that includes an FPGA 2802,RAM 2804, a USB interface 2806 and a USB bus 2808 connected to a hostcomputer. The system may include an interface board 2404 that includes aDUT vtt power regulator 2810, a DUT Vcc power regulator 2812, DUT clock,reset and communication circuitry 2814, DUT current sense and amplifier2816, and an analog-to-digital converter 2818. The system may alsoinclude a personality board 2406 that includes custom wireinter-connections 2820. The system may also include a device under test2822.

In the depicted embodiment, the development board is an AVNETdevelopment board. Specifically, the development board includes an LX16evaluation kit, Xilinx Spartan 6 field programmable gate array thatallows data from an analog to digital to be written to high speed RAM.The development board may include a USB or other interface for a hostcomputer 2408.

The personality board provides an interface for the electronic componentto the electronic component authentication system. In particular, thepersonality board includes an adaptor board 2409 and a chip carrier 2410that is configured to accept an electronic component. For example, anelectronic component may have a plurality of pins that connect to theelectronic component authentication system in a specified way. Forexample, FIG. 26 illustrates an integrated circuit with 10 pinsincluding 1 ground pin, 1 Vcc pin, and 1 external clock pin. Allelectronic components may not have the same pin configuration, forexample the ground, Vcc, or external clock pins on the integratedcircuit may be located at different positions. The personality board canmap the ground, Vcc, and external clock to the correct locations in theelectronic component authentication system. For example, the developmentboard may provide a ground, Vcc source, and external clock that arerouted through the interface board to the personality board.

FIG. 24 illustrates an installed personality board that maps pins tointerface board inputs. In the current embodiment, the personality boardis removable and different personality boards can be installed thatchange the routing of the signals from the electronic componentauthentication system to route signals to different pin positions withina chip carrier. That is, by having multiple personality boards the sameacquisition hardware can interface with multiple chips that do not sharethe same physical connection. FIG. 25 illustrates a plurality ofpersonality boards, where each personality board includes a chip carrierdesigned to accommodate a specific chip class. Instead of havingmultiple different personality boards, a single dynamic personalityboard may accommodate multiple chip classes by dynamically mapping thepin routing.

FIG. 15 illustrates one embodiment of a circuit diagram for apersonality board. The personality board can be used to provide a custominterconnection between an interface board and the device or componentunder test (DUT). For every DUT under consideration, its power, ground,clock and reset signals can be connected to the corresponding signals onthe interface board. In the case for MC68HC908 microcontrollers, the Vtttest voltage and the 1-wire communication signals can be connected sothe microcontroller runs in a monitor mode. The power connections can bemade by mounting zero ohm resistors to the pad corresponding to theappropriate DUT power pin and the Personality Board Vcc bus. Connectionsare likewise made to the DUT ground pins and the Personality Ground bus.Connection to the DUT clock is made by soldering a length of wire fromthe corresponding DUT clock pin to the personality board CLK pad (nozero ohm resistors are used in this case.) All other DUT signals made inlikewise fashion. All other DUT pins are unconnected.

The personality board may include a plurality of jumpers that can be setto map the various connection to a subject electronic componentaccording to its pin layout. The depicted personality board includes 32pins, in alternative constructions the personality board may includeadditional or fewer pins. The depicted personality board is a lowprofile quad flat package, though in alternative embodiments a differentpackage type may be utilized.

Depending on the intrinsic property data being collected, the interfaceboard may or may not include a sensor for measuring intrinsic propertydata about the electronic component. In the depicted embodiment, theinterface board includes a current or power sensor, an A/D unit forconverting the analog signal to a digital signal, a ground connection, aVcc or constant voltage source connection, and a signal generator. Thesignal generator can be used to standardize the intrinsic property datacollected from various electronic components. In the current embodiment,the signal generator is a clock signal that is provided to an externalclock input on the electronic component. The intrinsic property datacollection can be obtained synchronously with the clock signal. Inalternative embodiments, a different signal generator may replace theclock signal.

In some embodiments, a signal generator is unnecessary. For example, forsome components, such as passive or analog components, intrinsicproperty data can be collected by measuring input power on the powersource. By connecting one lead to a power source and another lead toground, the intrinsic noise waveform can be measured in an analogcomponent, which can be utilized to determine whether the unknowncomponents are authentic (similar to the collected intrinsic noisewaveform) or counterfeit (are not similar to the collected intrinsicnoise waveform). A similar process for creating identity vectors fordifferent classes of integrated circuits can be applied to passiveanalog components greatly expanding the range of electronic componentsto which the method described herein applies.

One embodiment of a circuit diagram for an interface board isillustrated in FIGS. 16A-16H. The interface board can providefunctionality to stimulate the Device Under Test (DUT) and to measurethe DUTs operating current. In the current embodiment, the interfaceboard is controlled by an FPGA board, such as a Xillinx Spartan-6 LX16Evaluation Kit. The functionality of the interface board can bedescribed as follows:

DUT Vtt Generation.

U10 and its associated components include a step-up DC/DC converterwhich can provide a “test voltage”, Vtt. The test voltage can be used bythe microcontroller, such as the MC68HC908 microcontroller, to enable a“monitor mode” of operation. This allows the microcontroller to run in aknown state. In alternative embodiments, the component may be configureddifferently in order to run in a known state. The DUT vtt generationcircuitry is also illustrated in the FIG. 28 block diagram as reference2810.

DUT Vcc Generation.

U7 and its associated components include a linear voltage regulator thatsupplies power, Vcc, to the DUT. The voltage can be configured byjumpers on the personality board to provide a particular voltage to theDUT. The DUT Vcc generation circuitry is also illustrated in the FIG. 28block diagram as reference 2812.

DUT Clock and Control Signals.

U5, U6, U13, U12, and U11 provide control signals to the DUT. Theseinclude the clock signal (U12), reset (U13), and 1-wire communication(U5 and U6.) These signals can be enabled by a general-purpose portexpander (U11.) The actual clock frequency and 1-wire serial data cab begenerated off-board by the FPGA. The DUT clock and control signalcircuitry is also illustrated in the FIG. 28 block diagram as reference2814.

DUT Power Current Detection and Amplification.

The power current of the DUT can be detected by the transformer T1. TheAC-coupled, high-frequency current signal is amplified byprogrammable-gain amplifier, U4. The DUT power current detection andamplification circuitry is also illustrated in the FIG. 28 block diagramas reference 2816.

Current Signal Digitization.

The amplified DUT power current is digitized by the high-speed,analog-to-digital converter (ADC), U15, and its associated components.The power for the ADC can be provided by the linear voltage regulator,U3. The high-speed digitized outputs of the ADC can be buffered by thedifferential line drivers, U14 and U16. The ADC output signals canconnect to the FPGA board where they are captured and saved to RAM. Thecurrent signal digitization circuitry is also illustrated in the FIG. 28block diagram as reference 2818.

Sample Clock Generation.

The sample clock for the ADC can be provided by the Phase-Locked-Loop(PLL) clock generator, U2 and its associated components. Power for thePLL can be provided by the linear voltage regulator, U1. The clockoutput of the PLL clocks both the ADC and the FPGA for synchronoussampling of DUT power current with DUT clock signal. The sample clockgeneration circuitry is also illustrated in the FIG. 28 block diagram asreference 2814.

The host computer connects to the development board and can receive theintrinsic property data, process the intrinsic property data, build aclassifier based on sample intrinsic property data of authenticcomponents, and use the classifier to identify whether a component iscounterfeit or authentic based on intrinsic property data about anunknown component.

FIG. 2 illustrates one embodiment of a counterfeit mitigation process200. The process includes receiving a plurality of components eachpurporting to be a particular component or component class 202. Anycomponents not belonging to a component class enrolled in the electroniccatalog are subjected to an enrollment procedure 204. The enrollmentprocedure includes pre-enrollment 206 and enrollment 220. In the currentembodiment, pre-enrollment is a multi-step process that includesselecting a pre-enrollment candidate sample 208 and capturingparameterized identify vector 210. For example, if multiple componentsfrom the same unenrolled component class are in the received inventory,the pre-enrollment process selects one of them as a candidate sample andobtains intrinsic property data about that sample. This may be done, forexample, by measuring a power draw signal, an electric field signal, ora magnetic field signal that is indicative of an intrinsic noisesignature. Once the intrinsic property data is collected, the sample canundergo destructive authentication 212 to determine whether the sampleis authentic 214. If the sample is not authentic, then that sample isquarantined 230, and a different sample of the same component class isselected, if available, and the process is repeated 216. If the sampleis authentic, then the intrinsic property data is used to enroll thatcomponent class into the electronic catalog so that future componentscan be classified based on the intrinsic property data captured duringpre-enrollment for this class of component 220. In some embodiments,pre-enrollment may include collection of multiple signals indicative ofintrinsic property data from the same or different authentic samplesfrom the subject component class.

If the component class of a component is enrolled in the electroniccatalog 222, then electronic catalog authentication is conducted todetermine whether the component is authentic 226. If the component isdetermined to be authentic, then the component is authenticatedinventory 228. If the component is determined to be counterfeit, thenthe component is quarantined 230.

FIG. 3 illustrates one embodiment of a method of training aclassification or electronic component authentication system 300. Themethod includes receiving a plurality of authentic IC training sets thatcan be used to build an authentic integrated circuit codebook orclassifier 302. The method includes selecting 304 one of the authenticintegrated circuit training sets 306 and installing each integratedcircuit in the set, one at a time, 308 into a test socket 310 orcomponent interface of a classification system. A signal generator 312can apply a signal, such as an external clock signal, to the authenticintegrated circuit and measure an intrinsic property of the integratedcircuit, such as a current draw signal that is representative of anintrinsic noise waveform 314. That waveform can be segmented 316 andparameterized 318 into an n-dimensional vector 320. That vector can thenbe stored as a recognized authentic IC 322 in an authentic IC codebookor classifier 324.

FIG. 4 illustrates one embodiment of a method of building a classifier400. The method includes acquiring data 404 from multiple componentsunder test (DUT) from different classes 402. The data acquired can be avariety of component specific deterministically random signals 406 suchas power consumption in the time domain 408, EM emission in the timedomain 410, or EM emissions in the spatial domain 412. When obtainedunder idle conditions, these signals are indicative of the intrinsicnoise signal inside the component, which is a unique signal that can actas a fingerprint for that component, or as a unique class signal forthat component class.

The data collected can be segmented into separate data streams in m,p-point samples 414. Each of the m, p-point segments can be transformedinto n-dimensional feature space Z 416. A transformation matrix, w, canbe created that has p dimensions ordered from most variance to least418. In one embodiment, this transformation matrix is created byconducting a principal component analysis of the entire feature space Z.This transformation matrix can be used to perform the clusteringanalysis 420 and build the classifier 422. The classifier may be testedby conducting a test with known authentic and known counterfeitcomponents to ensure the classifier provides the appropriate output 424.

In one embodiment, each deterministically random signal is about 500dimensions, taken from 500 point segments of the original signal. AfterPCA the 500 dimension vectors can be reduced to about 20 dimensions. Thecosine distance between these vectors can be used to compare them. Inalternative embodiments, a different distance calculation may be used tocompare the vectors.

Different types of sensors can be used to measure the different signals.FIG. 17 illustrates a block diagram of one embodiment of a magneticfield sensor. The sensor includes a faraday shield having two slitstoward one end for reducing eddy currents, a coiled wire connected to abulkhead. In the depicted embodiment, the faraday shield is a brass tubeand has a ⅛ inch diameter and the slit has a 0.01 inch width and ¼ inchlength. The coil is made from a 10T #34 AWG enameled wire, 0.06 inchdiameter dipped in epoxy and has a 50 ohm impedance. FIG. 18 illustratesa perspective view of the magnetic sensor. Different values or materialsmay be used in construction of a magnetic field sensor for alternativeembodiments. Further, different types of magnetic field sensors may beused in alternative embodiments.

FIG. 19 illustrates one embodiment of an electric field sensor. Thedepicted electric field sensor is a field effect transistor activee-field probe amplifier. In one embodiment, a short rod electric fieldprobe is connected to a high speed FET input operational amplifier, orother broadband high impedance buffer to achieve lock with the systemclock of the device under test as well as pick up data forclassification. The sensor amplifier may be enclosed for shielding straypickup. The electric sensor includes a high gain amplifier at afrequency of interest. In the current embodiment, the frequency ofinterest is in the 100 k-90 Mhz frequency band. FIG. 20 illustrates aperspective view of the FET active e-field probe amplifier.

The magnetic and electric field sensors can be used to measuremagnetic/electric fields in time and/or space. FIG. 21 illustrates asensor stand for positioning the sensor in a fixed position near thesubject electronic component. In the depicted embodiment, the sensor isconnected through an A/D unit to a host computer. FIG. 22 illustrates anx-y sensor stand for systematically varying the position of the sensorin order to obtain spatial magnetic or electric field readings. In oneembodiment, the x-y stand can be used to dynamically adjust the positionof the electric and/or magnetic sensor until a peak electric or magneticsensor value is reached. This may help to overcome minor differences ofepoxy in the packaging.

Directional terms, such as “vertical,” “horizontal,” “top,” “bottom,”“upper,” “lower,” “inner,” “inwardly,” “outer” and “outwardly,” are usedto assist in describing the invention based on the orientation of theembodiments shown in the illustrations. The use of directional termsshould not be interpreted to limit the invention to any specificorientation(s).

The above description is that of current embodiments of the invention.Various alterations and changes can be made without departing from thespirit and broader aspects of the invention as defined in the appendedclaims, which are to be interpreted in accordance with the principles ofpatent law including the doctrine of equivalents. This disclosure ispresented for illustrative purposes and should not be interpreted as anexhaustive description of all embodiments of the invention or to limitthe scope of the claims to the specific elements illustrated ordescribed in connection with these embodiments. For example, and withoutlimitation, any individual element(s) of the described invention may bereplaced by alternative elements that provide substantially similarfunctionality or otherwise provide adequate operation. This includes,for example, presently known alternative elements, such as those thatmight be currently known to one skilled in the art, and alternativeelements that may be developed in the future, such as those that oneskilled in the art might, upon development, recognize as an alternative.Further, the disclosed embodiments include a plurality of features thatare described in concert and that might cooperatively provide acollection of benefits. The present invention is not limited to onlythose embodiments that include all of these features or that provide allof the stated benefits, except to the extent otherwise expressly setforth in the issued claims. Any reference to claim elements in thesingular, for example, using the articles “a,” “an,” “the” or “said,” isnot to be construed as limiting the element to the singular.

The embodiments of the invention in which an exclusive property orprivilege is claimed are defined as follows:
 1. A method of detecting acounterfeit electronic component comprising: temporarily installing aplurality of known electronic components from one or more trustedsources into a component interface of an electronic componentauthentication system for signal measurement; measuring, during an idlestate, an intrinsic deterministically random noise signal for each ofthe plurality of known electronic components via the componentinterface; building a classifier based on the intrinsicdeterministically random noise signals of the plurality of knownelectronic components; non-destructively classifying an unknownelectronic component from an untrusted source using the classifier. 2.The method of detecting a counterfeit electronic component of claim 1wherein building a classifier includes: segmenting each intrinsicdeterministically random noise signal into an intrinsicdeterministically random noise vector; transforming each intrinsicdeterministically random noise vector into a feature vector; conductinga statistical analysis on each feature vector; and clustering thefeature vectors to create the classifier.
 3. The method of detecting acounterfeit electronic component of claim 2 wherein the feature vectoris time independent and spatially independent.
 4. The method ofdetecting a counterfeit electronic component of claim 2 wherein thefeature vector is in the frequency domain.
 5. The method of detecting acounterfeit electronic component of claim 2 wherein said conducting thestatistical analysis on the feature vector includes organizing thefeature vector by variance and discarding dimensions where the varianceis below a threshold.
 6. The method of detecting a counterfeitelectronic component of claim 2 wherein said conducting the statisticalanalysis on the feature vector includes organizing the feature vector byvariance and discarding all but a predefined number of dimensions thathave the highest variance.
 7. The method of detecting a counterfeitelectronic component of claim 2 wherein said clustering includesselecting a clustering algorithm based on properties of said unknownelectronic components.
 8. The method of detecting a counterfeitelectronic component of claim 2 wherein said clustering includescomparing the feature vectors to each other using a distance metric. 9.The method of detecting a counterfeit electronic component of claim 8wherein the distance metric includes Euclidean distance.
 10. The methodof detecting a counterfeit electronic component of claim 8 wherein thedistance metric includes cosine distance.
 11. The method of detecting acounterfeit electronic component of claim 8 wherein transforming eachintrinsic deterministically random noise vector into a feature vectorusing a hybrid transformation, wherein said hybrid transformationincludes transforming each intrinsic deterministically random noisevector using a plurality of different transformations that are combinedto create each feature vector.
 12. The method of detecting a counterfeitelectronic component of claim 1 wherein each of the plurality of knownelectronic components belongs to one of a plurality of chip classes. 13.The method of detecting a counterfeit electronic component of claim 1wherein measuring an intrinsic deterministically random noise signal foreach of a plurality of known electronic components from one or moretrusted sources includes measuring at least one of a current signal anda power signal.
 14. A system for classifying an unknown electroniccomponent comprising: an electronic component interface for electricallyinterfacing with the unknown electronic component; a sensor formeasuring a power consumption signal of the unknown electronic componentduring an idle state via said electronic component interface; a controlunit configured to: process a power consumption signal of the unknownelectronic component to generate a noise signature of the unknownelectronic component; retrieve previously-stored noise signatureinformation extracted from power consumption signals measured during anidle state of trusted authentic electronic components; compare saidnoise signature of the unknown electronic component and saidpreviously-stored noise signature information; and classify the unknownelectronic component based on the comparison.
 15. The system of claim 14wherein said unknown electronic component is at least one of a passivecomponent, an analog component, and a digital component.
 16. The systemof claim 14 wherein the system includes an apparatus for generating saidpreviously-stored noise signature information, said apparatus configuredto: measure an intrinsic deterministically random noise signal for eachof a plurality of electronic components during an idle state, whereinthe plurality of electronic components are authenticated or obtainedfrom a trusted source; segment each intrinsic deterministically randomnoise signal into an intrinsic deterministically random noise vector;transform each intrinsic deterministically random noise vector into afeature vector; conduct a statistical analysis on each feature vector;and cluster the feature vectors to generate noise signature information.17. The system of claim 16 wherein said feature vector is timeindependent and spatially independent.
 18. The system of claim 16wherein said feature vector is in the frequency domain.
 19. The systemof claim 16 wherein said statistical analysis on the feature vectorincludes organizing the feature vector by variance and discardingdimensions where the variance is below a threshold.
 20. The system ofclaim 16 wherein said statistical analysis on the feature vector toincludes organizing the feature vector by variance and discarding allbut a predefined number of dimensions that have the highest variance.21. The system of claim 16 wherein said clustering the feature vectorsincludes shared nearest neighbor clustering.
 22. The system of claim 16wherein said clustering includes comparing the feature vectors to eachother using a distance metric.
 23. The system of claim 22 wherein thedistance metric includes cosine distance.
 24. The system of claim 16wherein said apparatus is configured to transform each intrinsicdeterministically random noise vector into a feature vector using ahybrid transformation, wherein said hybrid transformation includestransforming each intrinsic deterministically random noise vector usinga plurality of different transformations that are combined to createeach feature vector.
 25. The system of claim 16 wherein measuring anintrinsic deterministically random noise signal for each of a pluralityof known electronic components from one or more trusted sources includesmeasuring at least one of a current signal and a power signal.
 26. Themethod of detecting a counterfeit electronic component of claim 1wherein the component interface includes connections for rail voltage,ground, and an external clock.
 27. The system of claim 14 wherein thecomponent interface includes connections for rail voltage, ground, andan external clock.
 28. A method of classifying a plurality of electroniccomponents comprising: temporarily installing a plurality of electroniccomponents into a component interface of an electronic componentauthentication system for signal measurement; measuring, during an idlestate, a power consumption signal for each electronic component via thecomponent interface; processing the power consumption signal of eachelectronic component to generate a deterministically random noisesignature of each of the plurality of electronic components; classifyingthe plurality of electronic components into different classes based onsimilarity of the deterministically random noise signatures.
 29. Themethod of claim 28 including: segmenting each intrinsicdeterministically random noise signature into an intrinsicdeterministically random noise vector; transforming each intrinsicdeterministically random noise vector into a feature vector; conductinga statistical analysis on each feature vector; and clustering thefeature vectors to create the different classes.
 30. The method of claim28 wherein at least some of the different classes represent differentgenerations of electronic components.